Fixes#89034
Updated tensor_to_numpy() function in tensor_numpy.cpp to handle ZeroTensors by throwing an error if force=False and returning an array full of zeros if force=True.
@ngimel, I just saw that you mentioned PyTorch is not too concerned with this issue but I had already worked on it so I figured I would push it anyways and see what you thought. Feel free to close the PR if you think it is not worth merging.
@albanD
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164487
Approved by: https://github.com/izaitsevfb
Fixes#89034
Updated tensor_to_numpy() function in tensor_numpy.cpp to handle ZeroTensors by throwing an error if force=False and returning an array full of zeros if force=True.
@ngimel, I just saw that you mentioned PyTorch is not too concerned with this issue but I had already worked on it so I figured I would push it anyways and see what you thought. Feel free to close the PR if you think it is not worth merging.
@albanD
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164487
Approved by: https://github.com/ngimel, https://github.com/albanD
Fixes#157720
### What's in this PR?
This PR improves the error handling in `torch.compile` for `ndarray.astype('O')` (or `object`). It now explicitly raises a `torch._dynamo.exc.Unsupported` exception with a clear explanation, instead of failing with a less intuitive error during fake tensor propagation.
This is achieved by adding a check within `NumpyNdarrayVariable.call_method` for this specific `astype` pattern.
A new test, `test_ndarray_astype_object_graph_break`, is also added to `test/test_numpy_interop.py` to verify this new behavior.
### Background
Previously, attempting to `torch.compile` a function containing `ndarray.astype('O')` would result in a `TorchRuntimeError` wrapping a `TypeError: data type 'O' not understood`. This error message, originating deep within the tensor mechanism, was not very user-friendly and didn't clearly state *why* it was unsupported.
This change makes the failure more explicit and provides a better user experience by giving a direct, actionable error message.
**Old Behavior (Error Traceback):**
```
torch.dynamo.exc.TorchRuntimeError: Dynamo failed to run FX node with fake tensors: ... got TypeError("data type 'O' not understood")
```
**New Behavior (Error Message):**
```
torch.dynamo.exc.Unsupported: ndarray.astype(object)
Explanation: ndarray.astype('O') or ndarray.astype(object) is not supported by torch.compile, as there is no equivalent to object type in torch.
```
### Testing
A new test has been added to `test_numpy_interop.py` which decorates a function containing `ndarray.astype("O")` with `torch.compile`. The test asserts that a `torch._dynamo.exc.Unsupported` exception is raised, confirming the new error handling works as expected.
The test can be run with:
`pytest test/test_numpy_interop.py -k test_ndarray_astype_object_graph_break`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/157810
Approved by: https://github.com/jansel
Related to #107302
The following test fails with NumPy 2.
```
_________ TestNumPyInteropCPU.test_numpy_array_interface_cpu __________
Traceback (most recent call last):
File "/usr/local/google/home/haifengj/git/pytorch_np2/test/test_numpy_interop.py", line 415, in test_numpy_array_interface
wrapped_x = np.array([1, -2, 3, -4], dtype=dtype)
OverflowError: Python integer -2 out of bounds for uint8
To execute this test, run the following from the base repo dir:
python test/test_numpy_interop.py TestNumPyInteropCPU.test_numpy_array_interface_cpu
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
```
According to the official warning from NumPy 1, the assigning negative value to a `uint8` is deprecated.
The recommended way is to `np.array([1, -2, 3, -4]).astype(np.uint8)`
See the following for details.
```
>>> np.array([1, -2, 3, -4], dtype=np.uint8)
<stdin>:1: DeprecationWarning: NumPy will stop allowing conversion of out-of-bound Python integers to integer arrays. The conversion of -2 to uint8 will fail in the future.
For the old behavior, usually:
np.array(value).astype(dtype)
will give the desired result (the cast overflows).
<stdin>:1: DeprecationWarning: NumPy will stop allowing conversion of out-of-bound Python integers to integer arrays. The conversion of -4 to uint8 will fail in the future.
For the old behavior, usually:
np.array(value).astype(dtype)
will give the desired result (the cast overflows).
array([ 1, 254, 3, 252], dtype=uint8)
```
This PR fixes the test failure.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137532
Approved by: https://github.com/soulitzer
We should support these to whatever extent we can. They corresponding
`torch.uint<w>` types are defined, so I don't see an issue with
generating the various casting rules and allowing them to trace.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125717
Approved by: https://github.com/lezcano
Fixes some files in #123062
Run lintrunner on files:
test/test_nnapi.py,
test/test_numba_integration.py,
test/test_numpy_interop.py,
test/test_openmp.py,
test/test_optim.py
```bash
$ lintrunner -a --take UFMT --all-files
ok No lint issues.
Successfully applied all patches.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126845
Approved by: https://github.com/ezyang
This is a lot of files changed! Don't panic! Here's how it works:
* Previously, we set `follow_imports = silent` for our mypy.ini configuration. Per https://mypy.readthedocs.io/en/stable/running_mypy.html#follow-imports, what this does is whenever we have an import to a module which is not listed as a file to be typechecked in mypy, we typecheck it as normal but suppress all errors that occurred in that file.
* When mypy is run inside lintrunner, the list of files is precisely the files covered by the glob in lintrunner.toml, but with files in excludes excluded.
* The top-level directive `# mypy: ignore-errors` instructs mypy to typecheck the file as normal, but ignore all errors.
* Therefore, it should be equivalent to set `follow_imports = normal`, if we put `# mypy: ignore-errors` on all files that were previously excluded from the file list.
* Having done this, we can remove the exclude list from .lintrunner.toml, since excluding a file from typechecking is baked into the files themselves.
* torch/_dynamo and torch/_inductor were previously in the exclude list, because they were covered by MYPYINDUCTOR. It is not OK to mark these as `# mypy: ignore-errors` as this will impede typechecking on the alternate configuration. So they are temporarily being checked twice, but I am suppressing the errors in these files as the configurations are not quite the same. I plan to unify the configurations so this is only a temporary state.
* There were some straggler type errors after these changes somehow, so I fixed them as needed. There weren't that many.
In the future, to start type checking a file, just remove the ignore-errors directive from the top of the file.
The codemod was done with this script authored by GPT-4:
```
import glob
exclude_patterns = [
...
]
for pattern in exclude_patterns:
for filepath in glob.glob(pattern, recursive=True):
if filepath.endswith('.py'):
with open(filepath, 'r+') as f:
content = f.read()
f.seek(0, 0)
f.write('# mypy: ignore-errors\n\n' + content)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118414
Approved by: https://github.com/thiagocrepaldi, https://github.com/albanD
**Reopened** to help with merge issues. See #59790 for full context.
Fixes#20778. Helps #71688.
Finalizes @martinPasen's force argument for `Tensor.numpy()`. It is set to False by default. If it's set to True then we:
1. detatch the Tensor, if requires_grad == True
2. move to cpu, if not on cpu already
3. Uses .resolve_conj() if .is_conj() == True
4. Uses .resolve_neg() if .is_neg() == True
cc @albanD
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78564
Approved by: https://github.com/albanD
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65331
ghstack-source-id: 148862595
This is a performance optimization for the use case:
```
tensor = torch.tensor(<large_data>, device='meta')
```
where the current implementation requires a superfluous memory allocation on CPU even though the target device is a meta.
Test Plan: Run existing tests since no behavioral change is introduced.
Reviewed By: ezyang
Differential Revision: D31055036
fbshipit-source-id: 04d6c13594a71fc65bf2fbd567ee71833a879851
(cherry picked from commit 489d0a151a5fc4f5a0d8e3e65897bf7d02affe4b)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63554
Following https://github.com/pytorch/pytorch/pull/61840#issuecomment-884087809, this deprecates all the dtype getters publicly exposed in the `torch.testing` namespace. The reason for this twofold:
1. If someone is not familiar with the C++ dispatch macros PyTorch uses, the names are misleading. For example `torch.testing.floating_types()` will only give you `float32` and `float64` skipping `float16` and `bfloat16`.
2. The dtype getters provide very minimal functionality that can be easily emulated by downstream libraries.
We thought about [providing an replacement](https://gist.github.com/pmeier/3dfd2e105842ad0de4505068a1a0270a), but ultimately decided against it. The major problem is BC: by keeping it, either the namespace is getting messy again after a new dtype is added or we need to somehow version the return values of the getters.
Test Plan: Imported from OSS
Reviewed By: H-Huang
Differential Revision: D30662206
Pulled By: mruberry
fbshipit-source-id: a2bdb10ab02ae665df1b5b76e8afa9af043bbf56
Summary:
This PR adds fixes mypy issues on the current pytorch main branch. In special, it replaces occurrences of `np.bool/np.float` to `np.bool_/np.float64`, respectively:
```
test/test_numpy_interop.py:145: error: Module has no attribute "bool"; maybe "bool_" or "bool8"? [attr-defined]
test/test_numpy_interop.py:159: error: Module has no attribute "float"; maybe "float_", "cfloat", or "float64"? [attr-defined]
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52090
Reviewed By: walterddr
Differential Revision: D26469596
Pulled By: malfet
fbshipit-source-id: e55a5c6da7b252469e05942e0d2588e7f92b88bf
Summary:
Improves one annotation for `augment_model_with_bundled_inputs`
Also add a comment to not work on caffe2 type annotations, that's not worth the effort - those ignores can stay as they are.
xref gh-16574
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49658
Reviewed By: heitorschueroff
Differential Revision: D25757721
Pulled By: ezyang
fbshipit-source-id: 44c396d8da9ef3f41b97f9c46a528f0431c4b463
Summary:
Creates multiple new test suites to have fewer tests in test_torch.py, consistent with previous test suite creation like test_unary_ufuncs.py and test_linalg.py.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47356
Reviewed By: ngimel
Differential Revision: D25202268
Pulled By: mruberry
fbshipit-source-id: 75fde3ca76545d1b32b86d432a5cb7a5ba8f5bb6
